• An Introduction to Statistical and Data Sciences via R
• Webpage: http://moderndive.com. GitHub Repo

• ModernDive's guiding philosophies are deeply intertwined our syllabi
• Hard to speak of ModernDive in isolation from syllabi and vice-versa
• Numbers are numbers, but data has context…

• Introduction to Statistical & Data Sciences: Webpage and GitHub Repo
• Administrative:
  • Chief non-econ/bio stats service class at Middlebury
  • 12 weeks each with 3h "lecture" + 1h "lab"
• Students:
  • ~24 students/section of all years/backgrounds. Only stats class many will take
  • Background: Many had AP stats, some with programming
  • All had laptops that they brought everyday

• Topic List
  • First half is data science: data visualization, manipulation, importing
  • Second half is intro stats: sampling, hypothesis tests, CI, regression
• Evaluation
  • 10%: weekly problem sets
  • 10%: engagement
  • 45%: 3 midterms (last during finals week)
  • 35%: Final projects

• First 10-15min: Priming topic, either via slides or chalk talk
• Remainder: Students read over text & do Learning Checks in groups and without direct instructor guidance.

What are we doing that's different and why?

1. Data first! Start with data science via tidyverse, then stats.
2. Replacing the mathematical/analytic with computational/simulation-based whenever possible.
3. The above necessitates algorithmic thinking, computational logic and some coding/programming.
4. Complete reproducibility

Actual dialogue I had with a student:

Cobb (TAS 2015): Minimizing prerequisites to research. In other words, focus on entirety of Wickham/Grolemund's pipeline…

… and not just this part.

Furthermore use data science tools that a data scientist would use. Example: tidyverse

What does this buy us?

• Context for asking scientific questions
• Look at data that's rich, real, and realistic. Examples: Data packages such as nycflights13 and fivethirtyeight
• Better motivate traditional statistical topics

Cobb (TAS 2015): Two possible "computational engines" for statistics, in particular relating to sampling:

• Mathematics: formulas, probability theory, large-sample approximations, central limit theorem
• Computers: simulations, resampling methods

We present students with a choice for our "engine":

Either we use this…	Or we use this…

• Almost all are thrilled to do latter
• Leave "bread crumbs" for more advanced math/stats courses

What does this buy us?

• Emphasizes: stats is not math, rather stats uses math.
• Simulations are more tactile
• Reducing probability and march to CLT, this frees up space in syllabus.

• Both "Data First!" and "Computers, Not Math!" necessitate algorithmic thinking, computational logic and some coding/programming.
• Battle is more psychological than anything:
  • "This is not a class on programming!"
  • "Computers are stupid!"
  • "Learning to code is like learning a foreign language!"
  • "Early on don't code from scratch! Take something else that's similar and tweak it!"
  • Learning how to Google effectively

Why should we do this?

• Data science and machine learning.
• Where statistics is heading. Gelman blog post.
• Many new tools, like DataCamp, allow us to outsource many less interesting topics to teach.
• Bigger picture:
  • Coding is becoming a basic skill like reading and writing.
  • Data analysis, as opposed to algorithms and data structures, could attract more students from traditionally underrepresented groups.

• Students learn best when they can take can apart a toy (analysis) and then rebuild it (synthesis).
• Crisis in Reproducibility
• Ultimately the best textbook is one you've written yourself.
  • Everyone has different contexts, backgrounds, needs
  • Hard to find one-size-fits-all solutions
• A new paradigm in textbooks? Versions, not editions?

Insert appropriate image

• Chester's Book: Ask how he wants it pitched
• DataCamp: Intro to R